Create app.py

app.py

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+import gradio as gr
+import ffmpeg
+import os
+import docx
+import warnings
+import assemblyai as aai
+import subprocess
+
+# Suppress FutureWarnings
+warnings.simplefilter("ignore", category=FutureWarning)
+
+Key = os.getenv("KeyA")  # Ensure this is set in your environment
+aai.settings.api_key = Key
+
+# Function to check if FFmpeg is installed
+def is_ffmpeg_installed():
+    try:
+        subprocess.run(["ffmpeg", "-version"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)
+        return True
+    except subprocess.CalledProcessError:
+        return False
+    except FileNotFoundError:
+        return False
+
+# Function to extract audio from video safely
+def extract_audio(video_path, output_audio_path="temp_audio.mp3"):
+    if not is_ffmpeg_installed():
+        raise RuntimeError("FFmpeg is not installed or not found in PATH.")
+
+    try:
+        ffmpeg.input(video_path).output(output_audio_path, format="mp3").run(overwrite_output=True, quiet=True)
+        return output_audio_path
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"FFmpeg error: {e.stderr.decode()}")
+
+# Function to transcribe audio using AssemblyAI
+def transcribe_audio(file):
+    ext = os.path.splitext(file.name)[-1].lower()
+    audio_path = "temp_audio.mp3"
+
+    # Extract audio if video is uploaded
+    if ext in [".mp4", ".avi", ".mov", ".mkv"]:
+        audio_path = extract_audio(file.name)
+    else:
+        audio_path = file.name  # Use audio file directly
+
+    # Upload file to AssemblyAI
+    transcriber = aai.Transcriber()
+    config = aai.TranscriptionConfig(speaker_labels=True)
+    transcript = transcriber.transcribe(audio_path, config=config)
+
+    return "\n".join([f"Speaker {utt.speaker}: {utt.text}" for utt in transcript.utterances])
+
+# Function to export transcription
+def save_transcription(text, file_format):
+    file_path = f"transcription.{file_format.lower()}"
+
+    if file_format == "TXT":
+        with open(file_path, "w") as f:
+            f.write(text)
+    elif file_format == "DOCX":
+        doc = docx.Document()
+        doc.add_paragraph(text)
+        doc.save(file_path)
+    elif file_format == "SRT":
+        with open(file_path, "w") as f:
+            for i, line in enumerate(text.split(".")):
+                start_time = f"00:00:{i*5:02d},000"
+                end_time = f"00:00:{(i+1)*5:02d},000"
+                f.write(f"{i+1}\n{start_time} --> {end_time}\n{line.strip()}\n\n")
+
+    return file_path
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎙️ Skroll - Audio & Video Transcription Tool")
+    gr.Markdown("Upload an audio or video file and transcribe. Export in .txt, .docx, or .srt format.")
+
+    file_input = gr.File(label="Upload Audio or Video")
+    transcript_output = gr.Textbox(label="Transcription", interactive=True, lines=10)
+    transcribe_btn = gr.Button("Transcribe")
+
+    with gr.Row():
+        file_format = gr.Dropdown(["TXT", "DOCX", "SRT"], label="Export Format")
+        export_btn = gr.Button("Export")
+
+    download_link = gr.File(label="Download Transcription")
+
+    # Define Actions
+    transcribe_btn.click(transcribe_audio, inputs=[file_input], outputs=transcript_output)
+    export_btn.click(save_transcription, inputs=[transcript_output, file_format], outputs=download_link)
+
+# Launch App
+demo.launch(debug=True)